Neural Decoding: Classifiers in actionmespanol/ClassSIAM.pdfNeural Decoding: Classifiers in action...
Transcript of Neural Decoding: Classifiers in actionmespanol/ClassSIAM.pdfNeural Decoding: Classifiers in action...
Neural Decoding: Classifiers in action
Malena I. Español
Kreiman Lab - Summer 2007
Experiment
Kreiman Lab - Summer 2007
Experiment
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We have many electrodes (channels)
Kreiman Lab - Summer 2007
Experiment
We show several pictures
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Kreiman Lab - Summer 2007
Categorization: Object
Kreiman Lab - Summer 2007
ResponseOptions:
N Areas N Highs (sign(x).max(|x|))Spectral Power (FFT+sum)(mean,std, median)(mean,max, maxposition)(mean, max, median)(min, max, max-min)(minposition, maxposition, minpos-maxpos)(max, maxposition, FFT+sum(Gamma))
Kreiman Lab - Summer 2007
ProcessSplit points in categoriesCount how many there are in eachCompute the smallest number sTake s points of each categoryChoose s/2 for training and s/2 for testingConstruct classifier using training pointsTest performance using testing pointsBootstrapping: shuffle categories
Kreiman Lab - Summer 2007
One Vs All (OVA)
Compute a binary classifier (one class vs. the rest of the classes) for each class.Take a test point and apply each binary classifier.Choose the class corresponding to the highest outcome.
Kreiman Lab - Summer 2007
Categorization
We have 7 categories:
1.Animals – 2.Chairs – 3.Faces –4.Fruits – 5.Legos – 6.Shoes – 7.Vehicles5 categories:
1.Animals – 2.Chairs – 3.Faces –4.Fruits – 5. old(7). Vehicles
Kreiman Lab - Summer 2007
Spectral Analysis
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
timeQuickTime™ and a
TIFF (Uncompressed) decompressorare needed to see this picture.
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture. QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
∑4-7.5 Hz∑ 8-13.5 Hz∑ 14-29.5 Hz∑ 30-58 Hz∑ 62-100 Hz
logFFT 2
Kreiman Lab - Summer 2007
Looking at the Data
Subject 7 Subject 8
Using MATLAB function “imagesc” we can take a quick look at the feature matrix
Kreiman Lab - Summer 2007
Choosing Channels
Weight of Binary Classifier for Faces vs. RestUsing FFT
Average of categories of Channel 67 with p-value
Subject 9
Kreiman Lab - Summer 2007
Choosing Bands
Channel 2 : 30-58 Hz64-100 Hz
Channel 3: 14-29.5 Hz
Subject 8
Channel 2
Channel 3Binary Classifier for Faces vs. Rest
Kreiman Lab - Summer 2007
Subject 10Channels:
42, 73, 76
73-77
73-76, 88
41, 73, 75, 88
41, 73, 81, 87
All Channels
Performance
0.582+- 0.016
Performance perCategory
1. 58 %2. 52 %3. 76 %4. 56 %5. 50 %
SelectedChannels
Performance
0.6463+- 0.015
Performance perCategory
1. 65 %2. 60 %3. 79 %4. 63 %5. 56 %
Kreiman Lab - Summer 2007
Subject 6
33, 41, 42, 43, 50
33
42, 49, 57
34, 42, 50
33, 42, 49
42
42, 50
Kreiman Lab - Summer 2007
Subject 6All ChannelsPerformance
0.4178+- 0.008
Performance perCategory
1. 47 %2. 38 %3. 49 %4. 39 %5. 41 %6. 44 %7. 34 %
Selected ChannelsPerformance
0.4973+- 0.015
Performance perCategory
1. 54 %2. 45 %3. 61 %4. 48 %5. 47 %6. 50 %7. 43 %
Kreiman Lab - Summer 2007
Subject 7Channels:
13, 101
100
75, 79, 80, 100, 101
12, 97, 99
75, 98
All Channels
Performance
0.3603+- 0.0252
Performance perCategory
1. 40 %2. 34 %3. 43 %4. 34 %5. 28 %
SelectedChannels
Performance
0.4530+- 0.029
Performance perCategory
1. 48 %2. 37 %3. 61 %4. 40 %5. 41 %
Kreiman Lab - Summer 2007
Subject 10: HighsChannels:
42, 73, 76
73-77
73-76, 88
41, 73, 75, 88
41, 73, 81, 87
All Channels
Performance
0.582+- 0.016
Performance perCategory
1. 58 %2. 52 %3. 76 %4. 56 %5. 50 %
SelectedChannels
Performance
0.6463+- 0.015
Performance perCategory
1. 65 %2. 60 %3. 79 %4. 63 %5. 56 %
Kreiman Lab - Summer 2007
Implementation
Important information:sr (Samples per second)Number of channelsNumber of categoriesNumber of trials
Kreiman Lab - Summer 2007
References Neural coding: computational and biophysical perspectives by GKA Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex by Serre, Kough, Cadieu, Knoblich, Kreiman and PoggioAn Introduction to Support Vector Machines by Cristianini, Shawe-Taylor